# YOLOLite

YOLOlite is a lightweight object detection model family from Roboflow, designed for low-latency deployments and edge hardware. You can train YOLOlite on a [Project](/workspaces/key-concepts.md) in Roboflow and deploy it via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md).

For self-hosted deployment, see [Roboflow Inference](https://inference.roboflow.com/).

YOLOlite input size is configured during training on Roboflow.

## Available variants

YOLOlite ships in two scaling families: a standard set and an edge-optimized set. Each is available in five sizes.

| Family   | Variants                                                                                       |
| -------- | ---------------------------------------------------------------------------------------------- |
| Standard | `yololite-n`, `yololite-s`, `yololite-m`, `yololite-l`, `yololite-xl`                          |
| Edge     | `yololite-edge-n`, `yololite-edge-s`, `yololite-edge-m`, `yololite-edge-l`, `yololite-edge-xl` |

You select a variant when training a YOLOlite model on a Project. The trained model is then served from the Serverless Hosted API under your `workspace/project/version` path.

## Code sample

Install the [Inference SDK](https://inference.roboflow.com/inference_helpers/inference_sdk/) and [supervision](https://supervision.roboflow.com/):

```bash
pip install inference-sdk supervision
```

The following sample runs detection against a YOLOlite model trained on a Roboflow Project, decodes the response with `supervision`, draws boxes and labels, and saves the annotated PNG. Replace `your-project/1` with your own `project/version`. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable.

```python
import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
results = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(results)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("annotated.png", annotated)
```

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `https://serverless.roboflow.com` for the Serverless Hosted API.
* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}


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